from sklearn_benchmarks.report import Reporting, ReportingHpo, print_time_report, print_env_info
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
print_time_report()
daal4py_KMeans_short: 0h 0m 1s
daal4py_Ridge: 0h 0m 2s
KMeans_short: 0h 0m 3s
daal4py_LogisticRegression: 0h 0m 4s
daal4py_KMeans_tall: 0h 0m 8s
Ridge: 0h 0m 11s
LogisticRegression: 0h 0m 21s
KMeans_tall: 0h 0m 24s
daal4py_KNeighborsClassifier_kd_tree: 0h 0m 29s
KNeighborsClassifier_kd_tree: 0h 2m 50s
daal4py_KNeighborsClassifier: 0h 2m 52s
HistGradientBoostingClassifier: 0h 5m 2s
lightgbm: 0h 5m 5s
catboost_lossguide: 0h 5m 9s
xgboost: 0h 5m 15s
catboost_symmetric: 0h 6m 34s
KNeighborsClassifier: 0h 36m 16s
total: 1h 10m 54s
print_env_info()
{
"system_info": {
"python": "3.8.10 | packaged by conda-forge | (default, May 11 2021, 07:01:05) [GCC 9.3.0]",
"executable": "/usr/share/miniconda/envs/sklbench/bin/python",
"machine": "Linux-5.4.0-1047-azure-x86_64-with-glibc2.10"
},
"dependencies_info": {
"pip": "21.1.2",
"setuptools": "49.6.0.post20210108",
"sklearn": "1.0.dev0",
"numpy": "1.20.3",
"scipy": "1.6.3",
"Cython": null,
"pandas": "1.2.4",
"matplotlib": "3.4.2",
"joblib": "1.0.1",
"threadpoolctl": "2.1.0"
},
"threadpool_info": [
{
"filepath": "/usr/share/miniconda/envs/sklbench/lib/libopenblasp-r0.3.15.so",
"prefix": "libopenblas",
"user_api": "blas",
"internal_api": "openblas",
"version": "0.3.15",
"num_threads": 2,
"threading_layer": "pthreads"
},
{
"filepath": "/usr/share/miniconda/envs/sklbench/lib/python3.8/site-packages/scikit_learn.libs/libgomp-f7e03b3e.so.1.0.0",
"prefix": "libgomp",
"user_api": "openmp",
"internal_api": "openmp",
"version": null,
"num_threads": 2
},
{
"filepath": "/usr/share/miniconda/envs/sklbench/lib/libgomp.so.1.0.0",
"prefix": "libgomp",
"user_api": "openmp",
"internal_api": "openmp",
"version": null,
"num_threads": 2
}
],
"cpu_count": 2
}
reporting = Reporting(config="config.yml")
reporting.run()
KNeighborsClassifier: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)¶All estimators share the following hyperparameters: algorithm=brute.
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | n_jobs | n_neighbors | accuracy_score_sklearn | accuracy_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.257 | 0.000 | 3.118 | 0.000 | 1 | 100 | NaN | NaN | 0.500 | 0.000 | 0.513 | 0.000 | See | See |
| 1 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 25.395 | 0.104 | 0.000 | 0.025 | 1 | 100 | 0.931 | 0.944 | 2.111 | 0.020 | 12.028 | 0.126 | See | See |
| 2 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.197 | 0.002 | 0.000 | 0.197 | 1 | 100 | 1.000 | 1.000 | 0.084 | 0.000 | 2.352 | 0.032 | See | See |
| 3 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.132 | 0.000 | 6.056 | 0.000 | 1 | 5 | NaN | NaN | 0.477 | 0.000 | 0.277 | 0.000 | See | See |
| 4 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 25.457 | 0.071 | 0.000 | 0.025 | 1 | 5 | 0.814 | 0.821 | 2.053 | 0.076 | 12.399 | 0.462 | See | See |
| 5 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.193 | 0.001 | 0.000 | 0.193 | 1 | 5 | 1.000 | 1.000 | 0.085 | 0.001 | 2.286 | 0.039 | See | See |
| 6 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.126 | 0.000 | 6.346 | 0.000 | -1 | 5 | NaN | NaN | 0.485 | 0.000 | 0.260 | 0.000 | See | See |
| 7 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 36.886 | 0.000 | 0.000 | 0.037 | -1 | 5 | 0.814 | 0.821 | 2.035 | 0.024 | 18.125 | 0.210 | See | See |
| 8 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.182 | 0.020 | 0.000 | 0.182 | -1 | 5 | 1.000 | 1.000 | 0.084 | 0.001 | 2.171 | 0.235 | See | See |
| 9 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.129 | 0.000 | 6.203 | 0.000 | -1 | 100 | NaN | NaN | 0.487 | 0.000 | 0.265 | 0.000 | See | See |
| 10 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 36.797 | 0.000 | 0.000 | 0.037 | -1 | 100 | 0.931 | 0.696 | 2.029 | 0.007 | 18.134 | 0.067 | See | See |
| 11 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.186 | 0.009 | 0.000 | 0.186 | -1 | 100 | 1.000 | 1.000 | 0.084 | 0.001 | 2.218 | 0.111 | See | See |
| 12 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.131 | 0.000 | 6.129 | 0.000 | 1 | 1 | NaN | NaN | 0.487 | 0.000 | 0.268 | 0.000 | See | See |
| 13 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 13.341 | 0.037 | 0.000 | 0.013 | 1 | 1 | 0.702 | 0.944 | 2.122 | 0.025 | 6.288 | 0.076 | See | See |
| 14 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.182 | 0.003 | 0.000 | 0.182 | 1 | 1 | 1.000 | 1.000 | 0.085 | 0.002 | 2.140 | 0.060 | See | See |
| 15 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.126 | 0.000 | 6.349 | 0.000 | -1 | 1 | NaN | NaN | 0.486 | 0.000 | 0.259 | 0.000 | See | See |
| 16 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 24.810 | 0.132 | 0.000 | 0.025 | -1 | 1 | 0.702 | 0.696 | 2.074 | 0.037 | 11.962 | 0.223 | See | See |
| 17 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.161 | 0.017 | 0.000 | 0.161 | -1 | 1 | 1.000 | 1.000 | 0.091 | 0.008 | 1.768 | 0.241 | See | See |
| 18 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.061 | 0.000 | 0.261 | 0.000 | 1 | 100 | NaN | NaN | 0.117 | 0.000 | 0.525 | 0.000 | See | See |
| 19 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 23.561 | 0.019 | 0.000 | 0.024 | 1 | 100 | 0.987 | 0.981 | 0.380 | 0.002 | 62.063 | 0.390 | See | See |
| 20 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.022 | 0.001 | 0.000 | 0.022 | 1 | 100 | 1.000 | 1.000 | 0.007 | 0.000 | 3.197 | 0.195 | See | See |
| 21 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.060 | 0.000 | 0.266 | 0.000 | 1 | 5 | NaN | NaN | 0.117 | 0.000 | 0.513 | 0.000 | See | See |
| 22 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 23.488 | 0.020 | 0.000 | 0.023 | 1 | 5 | 0.988 | 0.979 | 0.323 | 0.006 | 72.657 | 1.269 | See | See |
| 23 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.022 | 0.001 | 0.000 | 0.022 | 1 | 5 | 1.000 | 1.000 | 0.006 | 0.001 | 3.427 | 0.339 | See | See |
| 24 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.061 | 0.000 | 0.263 | 0.000 | -1 | 5 | NaN | NaN | 0.116 | 0.000 | 0.522 | 0.000 | See | See |
| 25 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 34.766 | 0.000 | 0.000 | 0.035 | -1 | 5 | 0.988 | 0.979 | 0.337 | 0.009 | 103.249 | 2.756 | See | See |
| 26 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.026 | 0.001 | 0.000 | 0.026 | -1 | 5 | 1.000 | 1.000 | 0.007 | 0.001 | 3.965 | 0.452 | See | See |
| 27 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.061 | 0.000 | 0.264 | 0.000 | -1 | 100 | NaN | NaN | 0.118 | 0.000 | 0.514 | 0.000 | See | See |
| 28 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 34.837 | 0.000 | 0.000 | 0.035 | -1 | 100 | 0.987 | 0.973 | 0.322 | 0.002 | 108.287 | 0.577 | See | See |
| 29 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.030 | 0.004 | 0.000 | 0.030 | -1 | 100 | 1.000 | 1.000 | 0.006 | 0.000 | 4.704 | 0.655 | See | See |
| 30 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.061 | 0.000 | 0.260 | 0.000 | 1 | 1 | NaN | NaN | 0.116 | 0.000 | 0.529 | 0.000 | See | See |
| 31 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 10.314 | 0.015 | 0.000 | 0.010 | 1 | 1 | 0.982 | 0.981 | 0.380 | 0.004 | 27.109 | 0.298 | See | See |
| 32 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.015 | 0.000 | 0.000 | 0.015 | 1 | 1 | 1.000 | 1.000 | 0.007 | 0.000 | 2.284 | 0.130 | See | See |
| 33 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.061 | 0.000 | 0.263 | 0.000 | -1 | 1 | NaN | NaN | 0.116 | 0.000 | 0.522 | 0.000 | See | See |
| 34 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 21.862 | 0.094 | 0.000 | 0.022 | -1 | 1 | 0.982 | 0.973 | 0.329 | 0.013 | 66.485 | 2.617 | See | See |
| 35 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.020 | 0.002 | 0.000 | 0.020 | -1 | 1 | 1.000 | 1.000 | 0.006 | 0.000 | 3.277 | 0.428 | See | See |
KNeighborsClassifier_kd_tree: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)¶All estimators share the following hyperparameters: algorithm=kd_tree.
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | n_jobs | n_neighbors | accuracy_score_sklearn | accuracy_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 3.410 | 0.000 | 0.023 | 0.000 | 1 | 1 | NaN | NaN | 0.744 | 0.000 | 4.581 | 0.000 | See | See |
| 1 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 0.796 | 0.006 | 0.000 | 0.001 | 1 | 1 | 0.948 | 0.960 | 0.111 | 0.001 | 7.153 | 0.091 | See | See |
| 2 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.002 | 0.001 | 0.000 | 0.002 | 1 | 1 | 1.000 | 1.000 | 0.000 | 0.000 | 8.519 | 4.268 | See | See |
| 3 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 3.326 | 0.000 | 0.024 | 0.000 | -1 | 5 | NaN | NaN | 0.743 | 0.000 | 4.475 | 0.000 | See | See |
| 4 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 0.891 | 0.013 | 0.000 | 0.001 | -1 | 5 | 0.963 | 0.973 | 0.577 | 0.004 | 1.543 | 0.024 | See | See |
| 5 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.006 | 0.001 | 0.000 | 0.006 | -1 | 5 | 0.000 | 1.000 | 0.001 | 0.000 | 5.233 | 1.786 | See | See |
| 6 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 3.373 | 0.000 | 0.024 | 0.000 | 1 | 5 | NaN | NaN | 0.749 | 0.000 | 4.501 | 0.000 | See | See |
| 7 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 1.496 | 0.008 | 0.000 | 0.001 | 1 | 5 | 0.963 | 0.973 | 0.574 | 0.003 | 2.608 | 0.021 | See | See |
| 8 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.005 | 0.003 | 0.000 | 0.005 | 1 | 5 | 0.000 | 1.000 | 0.001 | 0.000 | 4.007 | 2.642 | See | See |
| 9 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 3.409 | 0.000 | 0.023 | 0.000 | 1 | 100 | NaN | NaN | 0.751 | 0.000 | 4.540 | 0.000 | See | See |
| 10 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 4.950 | 0.019 | 0.000 | 0.005 | 1 | 100 | 0.960 | 0.972 | 0.199 | 0.002 | 24.823 | 0.305 | See | See |
| 11 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.007 | 0.001 | 0.000 | 0.007 | 1 | 100 | 0.000 | 1.000 | 0.000 | 0.000 | 17.940 | 6.989 | See | See |
| 12 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 3.355 | 0.000 | 0.024 | 0.000 | -1 | 100 | NaN | NaN | 0.753 | 0.000 | 4.456 | 0.000 | See | See |
| 13 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 2.908 | 0.022 | 0.000 | 0.003 | -1 | 100 | 0.960 | 0.960 | 0.110 | 0.002 | 26.335 | 0.417 | See | See |
| 14 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.010 | 0.001 | 0.000 | 0.010 | -1 | 100 | 0.000 | 1.000 | 0.000 | 0.000 | 39.618 | 16.762 | See | See |
| 15 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 3.334 | 0.000 | 0.024 | 0.000 | -1 | 1 | NaN | NaN | 0.749 | 0.000 | 4.451 | 0.000 | See | See |
| 16 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 0.485 | 0.010 | 0.000 | 0.000 | -1 | 1 | 0.948 | 0.972 | 0.200 | 0.003 | 2.420 | 0.063 | See | See |
| 17 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.004 | 0.001 | 0.000 | 0.004 | -1 | 1 | 1.000 | 1.000 | 0.000 | 0.000 | 10.521 | 4.355 | See | See |
| 18 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.883 | 0.000 | 0.018 | 0.000 | 1 | 1 | NaN | NaN | 0.520 | 0.000 | 1.697 | 0.000 | See | See |
| 19 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.027 | 0.001 | 0.001 | 0.000 | 1 | 1 | 0.977 | 0.972 | 0.001 | 0.000 | 33.492 | 9.986 | See | See |
| 20 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 1 | 1.000 | 1.000 | 0.000 | 0.000 | 5.677 | 3.993 | See | See |
| 21 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.870 | 0.000 | 0.018 | 0.000 | -1 | 5 | NaN | NaN | 0.525 | 0.000 | 1.658 | 0.000 | See | See |
| 22 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.033 | 0.004 | 0.000 | 0.000 | -1 | 5 | 0.988 | 0.982 | 0.008 | 0.001 | 4.399 | 0.594 | See | See |
| 23 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.003 | 0.001 | 0.000 | 0.003 | -1 | 5 | 1.000 | 1.000 | 0.000 | 0.000 | 19.516 | 13.177 | See | See |
| 24 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.865 | 0.000 | 0.018 | 0.000 | 1 | 5 | NaN | NaN | 0.526 | 0.000 | 1.645 | 0.000 | See | See |
| 25 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.029 | 0.000 | 0.001 | 0.000 | 1 | 5 | 0.988 | 0.982 | 0.007 | 0.000 | 3.882 | 0.244 | See | See |
| 26 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 5 | 1.000 | 1.000 | 0.000 | 0.000 | 5.181 | 3.446 | See | See |
| 27 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.878 | 0.000 | 0.018 | 0.000 | 1 | 100 | NaN | NaN | 0.579 | 0.000 | 1.516 | 0.000 | See | See |
| 28 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.059 | 0.000 | 0.000 | 0.000 | 1 | 100 | 0.991 | 0.980 | 0.001 | 0.000 | 43.902 | 16.108 | See | See |
| 29 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 100 | 1.000 | 1.000 | 0.000 | 0.000 | 5.698 | 3.814 | See | See |
| 30 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.869 | 0.000 | 0.018 | 0.000 | -1 | 100 | NaN | NaN | 0.562 | 0.000 | 1.547 | 0.000 | See | See |
| 31 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.051 | 0.001 | 0.000 | 0.000 | -1 | 100 | 0.991 | 0.972 | 0.001 | 0.000 | 54.242 | 16.028 | See | See |
| 32 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.003 | 0.001 | 0.000 | 0.003 | -1 | 100 | 1.000 | 1.000 | 0.000 | 0.000 | 27.568 | 21.172 | See | See |
| 33 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.908 | 0.000 | 0.018 | 0.000 | -1 | 1 | NaN | NaN | 0.555 | 0.000 | 1.637 | 0.000 | See | See |
| 34 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.033 | 0.004 | 0.000 | 0.000 | -1 | 1 | 0.977 | 0.980 | 0.001 | 0.001 | 22.476 | 8.664 | See | See |
| 35 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.002 | 0.000 | 0.000 | 0.002 | -1 | 1 | 1.000 | 1.000 | 0.000 | 0.000 | 20.342 | 14.136 | See | See |
KMeans_tall: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)¶All estimators share the following hyperparameters: algorithm=full, n_clusters=3, max_iter=30, n_init=1, tol=1e-16.
| estimator | function | n_samples_train | n_samples | n_features | n_iter_sklearn | mean_sklearn | stdev_sklearn | throughput | latency | init | adjusted_rand_score_sklearn | n_iter_daal4py | adjusted_rand_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KMeans_tall | fit | 1000000 | 1000000 | 2 | 30 | 0.682 | 0.0 | 0.703 | 0.000 | random | NaN | 30 | NaN | 0.468 | 0.0 | 1.458 | 0.000 | See | See |
| 1 | KMeans_tall | predict | 1000000 | 1000 | 2 | 30 | 0.002 | 0.0 | 0.316 | 0.000 | random | 0.001 | 30 | 0.001 | 0.000 | 0.0 | 8.995 | 5.487 | See | See |
| 2 | KMeans_tall | predict | 1000000 | 1 | 2 | 30 | 0.001 | 0.0 | 0.000 | 0.001 | random | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 11.189 | 7.387 | See | See |
| 3 | KMeans_tall | fit | 1000000 | 1000000 | 2 | 30 | 0.657 | 0.0 | 0.731 | 0.000 | k-means++ | NaN | 30 | NaN | 0.554 | 0.0 | 1.186 | 0.000 | See | See |
| 4 | KMeans_tall | predict | 1000000 | 1000 | 2 | 30 | 0.002 | 0.0 | 0.318 | 0.000 | k-means++ | 0.001 | 30 | 0.001 | 0.000 | 0.0 | 8.804 | 4.785 | See | See |
| 5 | KMeans_tall | predict | 1000000 | 1 | 2 | 30 | 0.001 | 0.0 | 0.000 | 0.001 | k-means++ | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 11.239 | 7.076 | See | See |
| 6 | KMeans_tall | fit | 1000000 | 1000000 | 100 | 30 | 6.548 | 0.0 | 3.665 | 0.000 | random | NaN | 30 | NaN | 2.905 | 0.0 | 2.254 | 0.000 | See | See |
| 7 | KMeans_tall | predict | 1000000 | 1000 | 100 | 30 | 0.002 | 0.0 | 12.806 | 0.000 | random | 0.002 | 30 | 0.002 | 0.000 | 0.0 | 6.602 | 2.504 | See | See |
| 8 | KMeans_tall | predict | 1000000 | 1 | 100 | 30 | 0.001 | 0.0 | 0.017 | 0.001 | random | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 10.822 | 6.472 | See | See |
| 9 | KMeans_tall | fit | 1000000 | 1000000 | 100 | 30 | 6.528 | 0.0 | 3.676 | 0.000 | k-means++ | NaN | 30 | NaN | 3.010 | 0.0 | 2.168 | 0.000 | See | See |
| 10 | KMeans_tall | predict | 1000000 | 1000 | 100 | 30 | 0.002 | 0.0 | 12.911 | 0.000 | k-means++ | 0.002 | 30 | 0.001 | 0.000 | 0.0 | 4.673 | 1.319 | See | See |
| 11 | KMeans_tall | predict | 1000000 | 1 | 100 | 30 | 0.001 | 0.0 | 0.016 | 0.001 | k-means++ | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 9.395 | 4.533 | See | See |
KMeans_short: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)¶All estimators share the following hyperparameters: algorithm=full, n_clusters=300, max_iter=20, n_init=1, tol=1e-16.
| estimator | function | n_samples_train | n_samples | n_features | n_iter_sklearn | mean_sklearn | stdev_sklearn | throughput | latency | init | adjusted_rand_score_sklearn | n_iter_daal4py | adjusted_rand_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KMeans_short | fit | 10000 | 10000 | 2 | 20 | 0.093 | 0.000 | 0.034 | 0.000 | random | NaN | 20 | NaN | 0.038 | 0.0 | 2.460 | 0.000 | See | See |
| 1 | KMeans_short | predict | 10000 | 1000 | 2 | 20 | 0.002 | 0.000 | 0.156 | 0.000 | random | 0.002 | 20 | 0.003 | 0.001 | 0.0 | 2.745 | 0.481 | See | See |
| 2 | KMeans_short | predict | 10000 | 1 | 2 | 20 | 0.001 | 0.000 | 0.000 | 0.001 | random | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 9.884 | 5.820 | See | See |
| 3 | KMeans_short | fit | 10000 | 10000 | 2 | 20 | 0.273 | 0.000 | 0.012 | 0.000 | k-means++ | NaN | 20 | NaN | 0.109 | 0.0 | 2.500 | 0.000 | See | See |
| 4 | KMeans_short | predict | 10000 | 1000 | 2 | 20 | 0.002 | 0.000 | 0.156 | 0.000 | k-means++ | 0.003 | 20 | 0.001 | 0.001 | 0.0 | 2.724 | 0.616 | See | See |
| 5 | KMeans_short | predict | 10000 | 1 | 2 | 20 | 0.002 | 0.001 | 0.000 | 0.002 | k-means++ | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 12.069 | 8.823 | See | See |
| 6 | KMeans_short | fit | 10000 | 10000 | 100 | 20 | 0.259 | 0.000 | 0.619 | 0.000 | random | NaN | 20 | NaN | 0.169 | 0.0 | 1.534 | 0.000 | See | See |
| 7 | KMeans_short | predict | 10000 | 1000 | 100 | 20 | 0.003 | 0.000 | 5.395 | 0.000 | random | 0.359 | 20 | 0.303 | 0.001 | 0.0 | 2.228 | 0.560 | See | See |
| 8 | KMeans_short | predict | 10000 | 1 | 100 | 20 | 0.002 | 0.000 | 0.010 | 0.002 | random | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 8.478 | 4.076 | See | See |
| 9 | KMeans_short | fit | 10000 | 10000 | 100 | 20 | 0.715 | 0.000 | 0.224 | 0.000 | k-means++ | NaN | 20 | NaN | 0.394 | 0.0 | 1.817 | 0.000 | See | See |
| 10 | KMeans_short | predict | 10000 | 1000 | 100 | 20 | 0.003 | 0.000 | 5.627 | 0.000 | k-means++ | 0.324 | 20 | 0.312 | 0.001 | 0.0 | 2.279 | 0.321 | See | See |
| 11 | KMeans_short | predict | 10000 | 1 | 100 | 20 | 0.002 | 0.001 | 0.008 | 0.002 | k-means++ | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 9.169 | 6.354 | See | See |
LogisticRegression: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)¶All estimators share the following hyperparameters: penalty=l2, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=nan, random_state=nan, solver=lbfgs, max_iter=100, multi_class=auto, verbose=0, warm_start=False, n_jobs=nan, l1_ratio=nan.
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | class_weight | l1_ratio | n_jobs | random_state | accuracy_score | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | LogisticRegression | fit | 1000000 | 1000000 | 100 | [20] | 11.721 | 0.0 | [-0.10066661] | 0.000 | NaN | NaN | NaN | NaN | NaN | 1.974 | 0.0 | 5.937 | 0.000 | See | See |
| 1 | LogisticRegression | predict | 1000000 | 1000 | 100 | [20] | 0.000 | 0.0 | [47.94809139] | 0.000 | NaN | NaN | NaN | NaN | 0.518 | 0.000 | 0.0 | 0.909 | 0.424 | See | See |
| 2 | LogisticRegression | predict | 1000000 | 1 | 100 | [20] | 0.000 | 0.0 | [0.18362037] | 0.000 | NaN | NaN | NaN | NaN | 0.000 | 0.000 | 0.0 | 0.442 | 0.330 | See | See |
| 3 | LogisticRegression | fit | 1000 | 1000 | 10000 | [26] | 0.866 | 0.0 | [2.40048964] | 0.001 | NaN | NaN | NaN | NaN | NaN | 0.753 | 0.0 | 1.150 | 0.000 | See | See |
| 4 | LogisticRegression | predict | 1000 | 100 | 10000 | [26] | 0.002 | 0.0 | [111.63860951] | 0.000 | NaN | NaN | NaN | NaN | 0.250 | 0.003 | 0.0 | 0.561 | 0.121 | See | See |
| 5 | LogisticRegression | predict | 1000 | 1 | 10000 | [26] | 0.000 | 0.0 | [18.29885728] | 0.000 | NaN | NaN | NaN | NaN | 0.000 | 0.001 | 0.0 | 0.149 | 0.086 | See | See |
Ridge: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)¶All estimators share the following hyperparameters: alpha=1.0, fit_intercept=True, normalize=deprecated, copy_X=True, max_iter=nan, tol=0.001, solver=auto, random_state=nan.
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | max_iter | random_state | r2_score | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Ridge | fit | 1000 | 1000 | 10000 | NaN | 0.193 | 0.0 | 0.415 | 0.0 | NaN | NaN | NaN | 0.195 | 0.0 | 0.991 | 0.000 | See | See |
| 1 | Ridge | predict | 1000 | 1000 | 10000 | NaN | 0.010 | 0.0 | 8.052 | 0.0 | NaN | NaN | 0.1 | 0.016 | 0.0 | 0.605 | 0.019 | See | See |
| 2 | Ridge | predict | 1000 | 1 | 10000 | NaN | 0.000 | 0.0 | 1.095 | 0.0 | NaN | NaN | NaN | 0.000 | 0.0 | 0.606 | 0.557 | See | See |
| 3 | Ridge | fit | 1000000 | 1000000 | 100 | NaN | 1.436 | 0.0 | 0.557 | 0.0 | NaN | NaN | NaN | 0.265 | 0.0 | 5.409 | 0.000 | See | See |
| 4 | Ridge | predict | 1000000 | 1000 | 100 | NaN | 0.000 | 0.0 | 5.316 | 0.0 | NaN | NaN | 1.0 | 0.000 | 0.0 | 0.629 | 0.469 | See | See |
| 5 | Ridge | predict | 1000000 | 1 | 100 | NaN | 0.000 | 0.0 | 0.012 | 0.0 | NaN | NaN | NaN | 0.000 | 0.0 | 0.637 | 0.603 | See | See |